The exploration of residents' perception of eco-urbanization at community and driving factors in China

Cities ◽  
2022 ◽  
Vol 122 ◽  
pp. 103513
Author(s):  
Chen Zeng ◽  
Xiangzheng Deng ◽  
Jing Yang ◽  
Yijiao Cheng ◽  
Tongxin Zhao ◽  
...  
Keyword(s):  
Driving Factors

MAKOTO AS THE INITIAL PRINCIPLE OF THE ETHICAL AND AESTHETIC BELIEFS OF THE JAPANESE

2020 ◽  
pp. 126-138
Author(s):  
M. P. Gerasimova ◽  
Keyword(s):  
Moral Obligation ◽  
Driving Factors ◽  
Chinese Characters ◽  
Spiritual Values ◽  
Conceptual Apparatus ◽  
Hieroglyphic Writing ◽  
The Aesthetic ◽  
The Relationship ◽  
Aesthetic Ideal

Makoto (まこと, lit.: truth, genuineness, reality, “realness”) is an element of the conceptual apparatus of the traditional worldview of the Japanese. In Japan, it is generally accepted that makoto is a philosophical and aesthetic concept that underlies Japanese spirituality, involving among other principles understanding of the order and laws of the truly existing Universum (shinrabansho̅; 森羅万象) and the universal interconnectedness of things (bambutsu ittai; 万物一体), the desire to understand the true essence of everything that person meets in life, and, unlike other spiritual values, is purely Shinto in origin. After getting acquainted with the Chinese hieroglyphic writing three Chinese characters were borrowed for the word makoto. Each of these characters means truthfulness, genuineness, but has its own distinctive nuances: 真 means truth, authenticity, truthfulness, 実 signifies truth, reality, essence, content, and 誠 again means truthfulness, sincerity, and truth. Makoto (“true words”) and makoto (“true deeds”) imply the highest degree of sincerity of words and honesty, correctness of thoughts, actions, and deeds. The relationship “true words — true deeds” can be seen as one of the driving factors of moral obligation, prompting everyone in their field, as well as in relations between people, to strive to be real. This desire contributed to the formation of a heightened sense of duty and responsibility among the Japanese, which became a hallmark of their character. However, makoto has not only ethical connotation, but aesthetic one as well, and can be considered as the basis on which were formed the concept of mono no aware (もののあ われ、 物の哀れ) and the aesthetic ideal of the same name, that became the first link in the chain of japanese perceptions of beauty. Each link in this chain is an expression of a new facet of makoto, which was revealed as a result of certain elements of the worldview that came to the fore in the historical era.

scholarly journals Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial form: A Case Study of Jiangsu Province, China

2021 ◽  
Vol 13 (6) ◽  
pp. 3121
Author(s):  
Guoping Xiong ◽  
Xin Cao ◽  
Nicholas A. S. Hamm ◽  
Tao Lin ◽  
Guoqin Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Regional Development ◽  
Driving Factors ◽  
Spatial Expansion ◽  
Spatial Form ◽  
Coordinated Development ◽  
Urbanization Level ◽  
Driving Mechanisms ◽  
Urbanization Development ◽  
Southern Jiangsu

Unbalanced regional development is widespread, and the imbalance of regional development in developing countries with rapid urbanization is increasingly apparent. This threatens the sustainable development of the region. Promoting the coordinated development of the region has become a hot spot of scientific research and a major practical need. Taking 99 counties of Jiangsu Province China, a typical coastal plain region, as the basic research unit, this paper explores the unbalanced development characteristics of the regional urban spatial form using three indicators: urban spatial expansion size, development intensity, and distribution aggregation degree. Then, their driving mechanisms were evaluated using spatial autocorrelation analysis, Pearson correlation analysis, linear regression, and geographically weighted regression. Our results found that the areas with larger urban spatial expansion size and development intensity were mainly concentrated in southern Jiangsu, where there was a positive spatial correlation between them. We found no agglomeration phenomenon in urban spatial distribution aggregation degree. From the perspective of driving factors: economics was the main driving factor of urban spatial expansion size; urbanization level and urbanization quality were the main driving factors of urban spatial development intensity. Natural landform and urbanization level are the main driving factors of urban spatial distribution aggregation degree. Finally, we discussed the optimization strategy of regional coordinated development. The quality of urbanization development and regional integration should be promoted in Southern Jiangsu. The level of urbanization development should be improved relying on rapid transportation to develop along the axis in central Jiangsu. The economic size should be increased, focusing on the expansion of the urban agglomeration in northern Jiangsu. This study will enrich the perspective of research on the characteristics and mechanisms of regional urban spatial imbalance, and helps to optimize and regulate the imbalance of regional urban development from multiple perspectives.

scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

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